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MedScan, a natural language processing engine for MEDLINE abstracts.

Svetlana Novichkova·Sergei Egorov·Nikolai Daraselia
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Abstract

MOTIVATION: The importance of extracting biomedical information from scientific publications is well recognized. A number of information extraction systems for the biomedical domain have been reported, but none of them have become widely used in practical applications. Most proposals to date make rather simplistic assumptions about the syntactic aspect of natural language. There is an urgent need for a system that has broad coverage and performs well in real-text applications. RESULTS: We present a general biomedical domain-oriented NLP engine called MedScan that efficiently processes sentences from MEDLINE abstracts and produces a set of regularized logical structures representing the meaning of each sentence. The engine utilizes a specially developed context-free grammar and lexicon. Preliminary evaluation of the system's performance, accuracy, and coverage exhibited encouraging results. Further approaches for increasing the coverage and reducing parsing ambiguity of the engine, as well as its application for information extraction are discussed.

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Abstracting and IndexingArtificial IntelligenceDatabase Management SystemsInformation Storage and RetrievalMEDLINENatural Language ProcessingPattern Recognition, AutomatedPeriodicals as TopicSubject HeadingsVocabulary, ControlledEvaluation StudyJournal ArticleValidation Study

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Svetlana Novichkova, Sergei Egorov, Nikolai Daraselia. 2003-09-01. MedScan, a natural language processing engine for MEDLINE abstracts.. https://doi.org/10.1093/bioinformatics%2Fbtg207

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